Picture (above): Rain over the Oro Valley. Credit: Dave Smith (Pixabay)

A major challenge in climate science is getting accurate rainfall estimates in areas where ground-based rain gauges are scarce. Satellite data is often used to estimate rainfall, but when these estimates are compared with data in areas where rain gauges exist, there is still a significant margin of error.

To reduce this error, a CLEX researcher and international colleagues have developed a hybrid approach to estimate recent rainfall that combines satellite-based rainfall estimates with satellite-based soil moisture estimates. When this approach was tested against independent rain gauge measurements it showed notable improvements, in a range of metrics when compared to other existing satellite-derived estimates.

This approach is particularly useful for estimating rainfall in data scarce regions, with improved estimates of recent rainfall (within the past 2-3 days) having multiple applications in agriculture, water resource management and flood forecasting.

  • Paper: Massari, C., Brocca, L., Pellarin, T., Abramowitz, G., Filippucci, P., Ciabatta, L., Maggioni, V., Kerr, Y., and Fernandez Prieto, D.: A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple satellite soil moisture products, Hydrol. Earth Syst. Sci., 24, 2687–2710, https://doi.org/10.5194/hess-24-2687-2020, 2020.